The mammalian target of rapamycin pathway is widely activated without <i>PTEN</i> deletion in renal cell carcinoma metastases
Bibliographic record
Abstract
BACKGROUND: Inhibitors of the mammalian target of rapamycin (mTOR) are emerging as promising therapies for metastatic renal cell carcinoma (RCC). Because rational treatment strategies require understanding the activation status of the underlying signaling pathway being targeted at the desired stage of disease, the authors examined the activation status of different components of the mTOR pathway in RCC metastases and matched primary tumors. METHODS: The authors immunostained metastatic RCC samples from 132 patients and a subset of 25 matched primary RCCs with antibodies against phosphatidylinositol 3'-kinase, PTEN, phospho-Akt, phospho-mTOR, and p70S6. PTEN genomic status was assessed by fluorescent in situ hybridization. Marker expression was correlated to clinicopathologic variables and to survival. RESULTS: The mTOR pathway showed widespread activation in RCC metastases of various sites with strong correlation between different components of this signaling cascade (P<.0001), but without significant PTEN genomic deletion. Only cytoplasmic phospho-mTOR showed independent prognostic significance (P = .029) and fidelity between primary RCCs and their matched metastases (P = .004). CONCLUSIONS: Activation of various components of the mTOR signaling pathway in metastatic RCC lesions across various tumor histologies, nuclear grades, and metastatic sites suggests the potential for vertical blockade of multiple steps of this pathway. Patient selection may be improved by mTOR immunostaining of primary RCC.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".